Organizations such as soft drink bottlers that distribute soft drinks or other products via vending machines typically operate these vending machines at geographically dispersed locations. Soft drink bottlers often distribute a variety of soft drink products to a city, region, or other area through a system or network of vending machines. A bottler may locate these vending machines at storefronts, building lobbies, neighborhood parks, movie theaters, beaches, or various other locations having requisite connectivity to an electrical power utility.
Preferred vending machine locations usually provide a robust flow of potential customers in the vicinity of the vending machine. The traffic of potential customers at certain locations is often more receptive to purchasing vending machine products than the traffic at other locations. For example, a soft drink vending machine located near a hot sports park might vend more soft drinks than a similar vending machine located in an air conditioned lobby. However, numerous factors may contribute to the relative performance impact of a vending machine's location. Representative factors can include the affluence of the potential customers that frequent the location, ambient temperature, nearby recreational activities, stability of the electrical power utility, competitive or complementary product offerings in nearby business outlets, environmental setting, nearby fixtures such as an adjacent bench, as well as numerous other known or unknown factors. The impact of location on the profitability of a vending machine provides a motivation for a vending machine operator to select locations that deliver strong financial results. However, selecting a financially rewarding location for a vending machine with little or no observed data, a priori, is often difficult based on conventional selection methods.
The factors affecting the desirability of a vending machine's location can be numerous and convoluted. Furthermore, the operational environment of a vending machine can be dynamic, varying with season, advertising campaigns, weather, competitive product introductions, and numerous other influences. In other words, a soft drink bottler or other vending machine operator has a limited ability to select vending machine locations that are likely to yield strong financial results using conventional selection methods. Conventional methods for selecting vending machine locations typically lack timely input to dynamic vending data and further lack a capability to consider multiple, interrelated factors associated with a vending machine's performance.
When the performance of a vending machine at a specific location changes, conventional vending machine operations often cannot readily identify the cause of the change and respond accordingly. If performance of a specific vending machine declines, implementing timely corrective measures would financially benefit operations. On the other hand, replicating conditions that caused a performance increase in a specific vending machine would also have a positive financial impact. However, conventional methods of managing vending machine operations typically do not aggregate information from each vending machine in a timely manner or process such information in a manner that can sufficiently correlate cause and effect to facilitate responsive action that is prompt and effective.
In addition to the overall vending performance of each vending machine, vending machine operations are also concerned with the mix of products that each vending machine offers and the stocking levels of these product offerings in each vending machine. The relative performance of each product offering in a vending machine usually depends upon numerous factors. Marketing-related influences include the occurrence of sales promotions, marketing campaigns, advertisements, and tie-ins to complementary events. Competitive influences can include the introduction of competitive products in nearby vending machines, competitive marketing campaigns, and price wars. Season can also significantly impact a product's relative contribution to total sales of a vending machine. For example, sport drink sales may increase in hot months and decline in colder times. Oftentimes, a change in product sales is not easily attributable to one or more specific causes. While the occurrence of certain events effecting vending performance are known in advance, other events can occur randomly and are not easily foreseen.
Vending machine operators have a financial motive to stock each vending machine with a mix of products that generates a high number of vends and a corresponding level of profit. However, conventional technology for tracking the sales of each product in each vending machine in a system of geographically dispersed vending machines often lacks sufficient specificity and detail to enable a vending machine operator to effectively adjust this product mix to respond to the dynamic environment in which vending machines frequently operate. Furthermore, conventional technologies for aggregating product-specific vending data from multiple vending machines and for analyzing such aggregated data generally cannot recommend product offerings in each vending machine in a geographically dispersed system of vending machines.
To address these representative deficiencies in the art, what is needed is a capability for collecting timely data from each vending machine in a system of vending machines, processing the collected data, and adjusting operational aspects of the vending machines based on the processed data. Such capabilities would benefit vending machine operators, such as soft drink bottlers, by promoting operational efficiency and enhancing profitability.